
"""simple docstring"""

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def      lowercase__    (       lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       Dict			)			->   Dict:






    """simple docstring"""




    if isinstance(lowerCAmelCase__			,					lowerCAmelCase__			):
        UpperCAmelCase          =       np.full((len(lowerCAmelCase__			), sequence_length, 2)			,					lowerCAmelCase__			)
    else:
        UpperCAmelCase          =       np.full((len(lowerCAmelCase__			), sequence_length)			,					lowerCAmelCase__			)

    for i, tensor in enumerate(lowerCAmelCase__			):
        if padding_side == "right":
            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__			):
                UpperCAmelCase          =       tensor[:sequence_length]
            else:
                UpperCAmelCase          =       tensor[:sequence_length]
        else:
            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__			):
                UpperCAmelCase          =       tensor[:sequence_length]
            else:
                UpperCAmelCase          =       tensor[:sequence_length]

    return out_tensor.tolist()




def      lowercase__    (       lowerCAmelCase					:       List[str]			)			->   List[str]:






    """simple docstring"""




    UpperCAmelCase          =       ord(lowerCAmelCase__			)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    UpperCAmelCase          =       unicodedata.category(lowerCAmelCase__			)
    if cat.startswith('P'			):
        return True
    return False







@dataclass
class   _UpperCAmelCase				(		_lowerCAmelCase				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						4_2
      __SCREAMING_SNAKE_CASE :				str				   =						True
      __SCREAMING_SNAKE_CASE :				Dict				   =						None
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						None
      __SCREAMING_SNAKE_CASE :				Dict				   =						-1_0_0
      __SCREAMING_SNAKE_CASE :				List[str]				   =						"pt"




      def      a_   (				self ,							lowercase_				)    ->       Dict:
          import torch

          UpperCAmelCase          =       """label""" if """label""" in features[0].keys() else """labels"""
          UpperCAmelCase          =       [feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase          =       self.tokenizer.pad(
              SCREAMING_SNAKE_CASE_ ,							padding=self.padding ,							max_length=self.max_length ,							pad_to_multiple_of=self.pad_to_multiple_of ,							return_tensors='pt' if labels is None else None ,							)

          if labels is None:
              return batch

          UpperCAmelCase          =       torch.tensor(batch['entity_ids']				).shape[1]
          UpperCAmelCase          =       self.tokenizer.padding_side
          if padding_side == "right":
              UpperCAmelCase          =       [
                  list(SCREAMING_SNAKE_CASE_				) + [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_				)) for label in labels
              ]
          else:
              UpperCAmelCase          =       [
                  [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_				)) + list(SCREAMING_SNAKE_CASE_				) for label in labels
              ]

          UpperCAmelCase          =       [feature["""ner_tags"""] for feature in features]
          UpperCAmelCase          =       padding_tensor(SCREAMING_SNAKE_CASE_ ,							-1 ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       [feature["""original_entity_spans"""] for feature in features]
          UpperCAmelCase          =       padding_tensor(SCREAMING_SNAKE_CASE_ ,							(-1, -1) ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_				)
          UpperCAmelCase          =       {k: torch.tensor(SCREAMING_SNAKE_CASE_ ,							dtype=torch.intaa				) for k, v in batch.items()}

          return batch








def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






